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Monitoring the fitness of antiviral-resistant influenza strains during an epidemic: a mathematical modelling study.

ABSTRACT: Antivirals (eg, oseltamivir) are important for mitigating influenza epidemics. In 2007, an oseltamivir-resistant influenza seasonal A H1N1 strain emerged and spread to global fixation within 1 year. This event showed that antiviral-resistant (AVR) strains can be intrinsically more transmissible than their contemporaneous antiviral-sensitive (AVS) counterpart. Surveillance of AVR fitness is therefore essential. Our objective was to develop a simple method for estimating AVR fitness from surveillance data.We defined the fitness of AVR strains as their reproductive number relative to their co-circulating AVS counterparts. We developed a simple method for real-time estimation of AVR fitness from surveillance data. This method requires only information on generation time without other specific details regarding transmission dynamics. We first used simulations to validate this method by showing that it yields unbiased and robust fitness estimates in most epidemic scenarios. We then applied this method to two retrospective case studies and one hypothetical case study.We estimated that the oseltamivir-resistant A H1N1 strain that emerged in 2007 was 4% (95% credible interval [CrI] 3-5) more transmissible than its oseltamivir-sensitive predecessor and the oseltamivir-resistant pandemic A H1N1 strain that emerged and circulated in Japan during 2013-14 was 24% (95% CrI 17-30) less transmissible than its oseltamivir-sensitive counterpart. We show that in the event of large-scale antiviral interventions during a pandemic with co-circulation of AVS and AVR strains, our method can be used to inform optimal use of antivirals by monitoring intrinsic AVR fitness and drug pressure on the AVS strain.We developed a simple method that can be easily integrated into contemporary influenza surveillance systems to provide reliable estimates of AVR fitness in real time.Research Fund for the Control of Infectious Disease (09080792) and a commissioned grant from the Health and Medical Research Fund from the Government of the Hong Kong Special Administrative Region, Harvard Center for Communicable Disease Dynamics from the National Institute of General Medical Sciences (grant number U54 GM088558), Area of Excellence Scheme of the Hong Kong University Grants Committee (grant number AoE/M-12/06).

SUBMITTER: Leung K

PROVIDER: S-EPMC5470942 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature

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Monitoring the fitness of antiviral-resistant influenza strains during an epidemic: a mathematical modelling study.

Leung Kathy K Lipsitch Marc M Yuen Kwok Yung KY Wu Joseph T JT

The Lancet. Infectious diseases 20161201 3

<h4>Background</h4>Antivirals (eg, oseltamivir) are important for mitigating influenza epidemics. In 2007, an oseltamivir-resistant influenza seasonal A H1N1 strain emerged and spread to global fixation within 1 year. This event showed that antiviral-resistant (AVR) strains can be intrinsically more transmissible than their contemporaneous antiviral-sensitive (AVS) counterpart. Surveillance of AVR fitness is therefore essential. Our objective was to develop a simple method for estimating AVR fit ...[more]

PMID: 27914853

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Project description:BackgroundCo-circulation of influenza strains is common to seasonal epidemics and pandemic emergence. Competition was considered involved in the vicissitudes of co-circulating influenza strains but never quantitatively studied at the human population level. The main purpose of the study was to explore the competition dynamics of co-circulating influenza strains in a quantitative way.MethodsWe constructed a heterogeneous dynamic transmission model and ran the model to fit the weekly A/H1N1 influenza virus isolation rate through an influenza season. The construction process started on the 2007-2008 single-clade influenza season and, with the contribution from the clade-based A/H1N1 epidemiological curves, advanced to the 2008-2009 two-clade influenza season. Pearson method was used to estimate the correlation coefficient between the simulated epidemic curve and the observed weekly A/H1N1 influenza virus isolation rate curve.ResultsThe model found the potentially best-fit simulation with correlation coefficient up to 96% and all the successful simulations converging to the best-fit. The annual effective reproductive number of each co-circulating influenza strain was estimated. We found that, during the 2008-2009 influenza season, the annual effective reproductive number of the succeeding A/H1N1 clade 2B-2, carrying H275Y mutation in the neuraminidase, was estimated around 1.65. As to the preceding A/H1N1 clade 2C-2, the annual effective reproductive number would originally be equivalent to 1.65 but finally took on around 0.75 after the emergence of clade 2B-2. The model reported that clade 2B-2 outcompeted for the 2008-2009 influenza season mainly because clade 2C-2 suffered from a reduction of transmission fitness of around 71% on encountering the former.ConclusionsWe conclude that interdisciplinary data-driven mathematical modelling could bring to light the transmission dynamics of the A/H1N1 H275Y strains during the 2007-2009 influenza seasons worldwide and may inspire us to tackle the continually emerging drug-resistant A/H1N1pdm09 strains. Furthermore, we provide a prospective approach through mathematical modelling to solving a seemingly unintelligible problem at the human population level and look forward to its application at molecular level through bridging the resolution capacities of related disciplines.

Project description:BACKGROUND:Many countries have amassed antiviral stockpiles for pandemic preparedness. Despite extensive trial data and modelling studies, it remains unclear how to make optimal use of antiviral stockpiles within the constraints of healthcare infrastructure. Modelling studies informed recommendations for liberal antiviral distribution in the pandemic phase, primarily to prevent infection, but failed to account for logistical constraints clearly evident during the 2009 H1N1 outbreaks. Here we identify optimal delivery strategies for antiviral interventions accounting for logistical constraints, and so determine how to improve a strategy's impact. METHODS AND FINDINGS:We extend an existing SEIR model to incorporate finite diagnostic and antiviral distribution capacities. We evaluate the impact of using different diagnostic strategies to decide to whom antivirals are delivered. We then determine what additional capacity is required to achieve optimal impact. We identify the importance of sensitive and specific case ascertainment in the early phase of a pandemic response, when the proportion of false-positive presentations may be high. Once a substantial percentage of ILI presentations are caused by the pandemic strain, identification of cases for treatment on syndromic grounds alone results in a greater potential impact than a laboratory-dependent strategy. Our findings reinforce the need for a decentralised system capable of providing timely prophylaxis. CONCLUSIONS:We address specific real-world issues that must be considered in order to improve pandemic preparedness policy in a practical and methodologically sound way. Provision of antivirals on the scale proposed for an effective response is infeasible using traditional public health outbreak management and contact tracing approaches. The results indicate to change the transmission dynamics of an influenza epidemic with an antiviral intervention, a decentralised system is required for contact identification and prophylaxis delivery, utilising a range of existing services and infrastructure in a "whole of society" response.

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Monitoring the fitness of antiviral-resistant influenza strains during an epidemic: a mathematical modelling study.

Publications

Monitoring the fitness of antiviral-resistant influenza strains during an epidemic: a mathematical modelling study.

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